Rapid parameter estimation for selective inversion recovery myelin imaging using an open-source Julia toolkit

Abstract

Background: Magnetic resonance imaging (MRI) is used extensively to quantify myelin content, however computational bottlenecks remain challenging for advanced imaging techniques in clinical settings. We present a fast, open-source toolkit for processing quantitative magnetization transfer derived from selective inversion recovery (SIR) acquisitions that allows parameter map estimation, including the myelin-sensitive macromolecular pool size ratio (PSR). Significant progress has been made in reducing SIR acquisition times to improve clinically feasibility. However, parameter map estimation from the resulting data remains computationally expensive. To overcome this computational limitation, we developed a computationally efficient, open-source toolkit implemented in the Julia language. Methods: To test the accuracy of this toolkit, we simulated SIR images with varying PSR and spin-lattice relaxation time of the free water pool (R1f) over a physiologically meaningful scale from 5 to 20% and 0.5 to 1.5 s-1, respectively. Rician noise was then added, and the parameter maps were estimated using our Julia toolkit. Probability density histogram plots and Lin's concordance correlation coefficients (LCCC) were used to assess accuracy and precision of the fits to our known simulation data. To further mimic biological tissue, we generated five cross-linked bovine serum albumin (BSA) phantoms with concentrations that ranged from 1.25 to 20%. The phantoms were imaged at 3T using SIR, and data were fit to estimate PSR and R1f. Similarly, a healthy volunteer was imaged at 3T, and SIR parameter maps were estimated to demonstrate the reduced computational time for a real-world clinical example. Results: Estimated SIR parameter maps from our Julia toolkit agreed with simulated values (LCCC> 0.98). This toolkit was further validated using BSA phantoms and a whole brain scan at 3T. In both cases, SIR parameter estimates were consistent with published values using MATLAB. However, compared to earlier work using MATLAB, our Julia toolkit provided an approximate 20-fold reduction in computational time. Conclusions: Presented here, we developed a fast, open-source, toolkit for rapid and accurate SIR MRI using Julia. The reduction in computational cost should allow SIR parameters to be accessible in clinical settings.

Rapid whole‐brain myelin imaging with selective inversion recovery and compressed SENSE

“…Substituting Equation ( 12) into Equation ( 13) and using Equations ( 5)-( 9), the resultant longitudinal magnetization of the free pool M f (t i ) contains seven parameters: R 1f , R 1m , PSR, k mf , S f , S m , and M f (∞), which can be estimated by fitting the measured data to the above bi-exponential model. 21,22,40 During fitting, k mf can be assumed a constant value (= 12.5 s −1 ) as it was shown to be relatively consistent in normal and diseased neural tissue. 41 Furthermore, it has been shown errors in the assumed k mf value do not significantly bias estimates of the other model parameters when the optimal sampling strategies are used.…”

“…And finally, at the end of the inversion time, the longitudinal magnetization of the free pool (that is proportional to the acquired signal intensity) is $$$$ {M}_f\left({t}_i\right)=\left({b}_f^{+}\left({t}_i\right){e}^{-{R}_1^{+}{t}_i}+{b}_f^{-}\left({t}_i\right){e}^{-{R}_1^{-}{t}_i}+1\right)\cdot {M}_f\left(\infty \right). $$$$ Substituting Equation () into Equation () and using Equations (), the resultant longitudinal magnetization of the free pool $$$ {M}_f\left({t}_i\right) $$$ contains seven parameters: $$$ {R}_{1f} $$$, $$$ {R}_{1m} $$$, $$$ PSR $$$, $$$ {k}_{mf} $$$, $$$ {S}_f $$$, $$$ {S}_m $$$, and $$$ {M}_f\left(\infty \right) $$$, which can be estimated by fitting the measured data to the above bi‐exponential model 21,22,40 …”